This paper described an automated pattern generator to generate various synthetic data sets for classification prob- lems, where the problem's complexity can be manipulated autonomously. The Tabu Search technique has been applied in the pattern generator to discover the best combination of domain features in order to adjust the complexity levels of the problem. Experiments confirm that the pattern genera- tor was able to tune the problem's complexity so that it can either increase or decrease the classification performance. The novel contributions in this work enable the effect of domain features that alter classification performance, to be- come human readable. This work provides a new method for generating artificial datasets at various l...
International audienceFeature selection in classification can be modeled as a com-binatorial optimiz...
An enormous amount of knowledge is needed to infer the meaning of unrestricted natural language. The...
Combinatorial optimisation problems are known as unpredictable and challenging due to their nature a...
This paper described an automated pattern generator to generate various synthetic data sets for clas...
In producing an artificial dataset, humans usually play a major role in creating and controlling the...
Classifying objects and patterns to a certain category is crucial for both humans and machines, so t...
This thesis introduces a Three-Cornered Coevolution System that is capable of addressing classificat...
Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in...
In this paper, we highlight the use of synthetic data sets to analyze learners behavior under bounde...
We design several algorithms representing evaluation processes of different complexity, ranging from...
Classifying objects and patterns to certain categories is crucial for both humans and machines. Patt...
In this paper, we consider the problem of adapting statistical classifiers trained from some source ...
In the field of data-mining, symbolic techniques have produced optimal solutions, which are expected...
We are surrounded by ubiquitous and interconnected soft- ware systems which gather valuable data. Th...
Machines capable of automatic pattern recognition have many fascinating uses. Algorithms for supervi...
International audienceFeature selection in classification can be modeled as a com-binatorial optimiz...
An enormous amount of knowledge is needed to infer the meaning of unrestricted natural language. The...
Combinatorial optimisation problems are known as unpredictable and challenging due to their nature a...
This paper described an automated pattern generator to generate various synthetic data sets for clas...
In producing an artificial dataset, humans usually play a major role in creating and controlling the...
Classifying objects and patterns to a certain category is crucial for both humans and machines, so t...
This thesis introduces a Three-Cornered Coevolution System that is capable of addressing classificat...
Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in...
In this paper, we highlight the use of synthetic data sets to analyze learners behavior under bounde...
We design several algorithms representing evaluation processes of different complexity, ranging from...
Classifying objects and patterns to certain categories is crucial for both humans and machines. Patt...
In this paper, we consider the problem of adapting statistical classifiers trained from some source ...
In the field of data-mining, symbolic techniques have produced optimal solutions, which are expected...
We are surrounded by ubiquitous and interconnected soft- ware systems which gather valuable data. Th...
Machines capable of automatic pattern recognition have many fascinating uses. Algorithms for supervi...
International audienceFeature selection in classification can be modeled as a com-binatorial optimiz...
An enormous amount of knowledge is needed to infer the meaning of unrestricted natural language. The...
Combinatorial optimisation problems are known as unpredictable and challenging due to their nature a...